import json
import os
import re

import torch.distributed as dist

import utils


def pre_caption(caption, max_words=50):
    caption = re.sub(
        r"([.!\"()*#:;~])",
        ' ',
        caption.lower(),
    )
    caption = re.sub(
        r"\s{2,}",
        ' ',
        caption,
    )
    caption = caption.rstrip('\n')
    caption = caption.strip(' ')

    # truncate caption
    caption_words = caption.split(' ')
    if len(caption_words) > max_words:
        caption = ' '.join(caption_words[:max_words])

    return caption


def pre_question(question, max_ques_words=50):
    question = re.sub(
        r"([.!\"()*#:;~])",
        '',
        question.lower(),
    )
    question = question.rstrip(' ')

    # truncate question
    question_words = question.split(' ')
    if len(question_words) > max_ques_words:
        question = ' '.join(question_words[:max_ques_words])

    return question


def save_result(result, result_dir, filename, remove_duplicate=''):
    result_file = os.path.join(result_dir, '%s_rank%d.json' % (filename, utils.get_rank()))
    final_result_file = os.path.join(result_dir, '%s.json' % filename)

    json.dump(result, open(result_file, 'w'))

    dist.barrier()

    if utils.is_main_process():
        # combine results from all processes
        result = []

        for rank in range(utils.get_world_size()):
            result_file = os.path.join(result_dir, '%s_rank%d.json' % (filename, rank))
            res = json.load(open(result_file, 'r'))
            result += res

        if remove_duplicate:
            result_new = []
            id_list = []
            for res in result:
                if res[remove_duplicate] not in id_list:
                    id_list.append(res[remove_duplicate])
                    result_new.append(res)
            result = result_new

        json.dump(result, open(final_result_file, 'w'))
        print('result file saved to %s' % final_result_file)

    return final_result_file


from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
from torchvision.datasets.utils import download_url


def coco_caption_eval(coco_gt_root, results_file, split):
    urls = {'val': 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json',
            'test': 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json'}
    filenames = {'val': 'coco_karpathy_val_gt.json', 'test': 'coco_karpathy_test_gt.json'}

    download_url(urls[split], coco_gt_root)
    annotation_file = os.path.join(coco_gt_root, filenames[split])

    # create coco object and coco_result object
    coco = COCO(annotation_file)
    coco_result = coco.loadRes(results_file)

    # create coco_eval object by taking coco and coco_result
    coco_eval = COCOEvalCap(coco, coco_result)

    # evaluate on a subset of images by setting
    # coco_eval.params['image_id'] = coco_result.getImgIds()
    # please remove this line when evaluating the full validation set
    # coco_eval.params['image_id'] = coco_result.getImgIds()

    # evaluate results
    # SPICE will take a few minutes the first time, but speeds up due to caching
    coco_eval.evaluate()

    # print output evaluation scores
    for metric, score in coco_eval.eval.items():
        print(f'{metric}: {score:.3f}')

    return coco_eval
